While trying to detect small or slow-moving targets, the target detection ability of a radar platform, such as a ground moving radar or a maritime surface search radar minimum detectable velocity, is limited by the radar dwell duration azimuth-Doppler extent of any clutter in the background. A basic problem with utilizing the data produced by such radars is that the desired information, such as radar scattering from a person or small vessel, can be obscured by clutters due to radar reflections from the clutters.
The problem is exacerbated by factors such as short duration dwells, wind-blown ground clutter, rain clutter, and bird-flock clutter and radio frequency interference (RFI). It can be difficult to separate target from clutter returns when the clutter is spread in Doppler, in which target and clutter returns overlap in Doppler. The clutter (and other non-target signals) can be Doppler spread due to factors such as: radar platform motion; the nature of the clutter, such as whether it is wind blow, rain, bird flock, sea, etc.; or other factors such as miscalibration and RFI. The target trackers or clutter maps can be overwhelmed by a very large number of clutter-hit detections. Furthermore, for a small size target, it becomes increasingly difficult to distinguish the target from the non-stationary clutter radar return signal.
Most textbook target detection techniques assume a target embedded in independent, Gaussian noise. However, many real-world problems do not conform to these assumptions, such as detection of small targets in sea clutter.
A traditional technique to detect endo-clutter targets is Space-Time Adaptive Processing (STAP). The STAP technique combines adaptive beamforming and adaptive Doppler filtering into a single 2-D algorithm to yield 2-D detection weights for a target at each candidate azimuth and Doppler. A primary disadvantage of this method is that determination of adaptive weights requires stationary interference and training data that adequately captures the space-time correlation of such interference. Performance of STAP may be deleteriously impacted by signal interference that is difficult to train on, such as non-stationary clutter and terrain bounced interference. Furthermore, the STAP method requires large number of radar return snapshots for training.
Accordingly, it is desirable to provide a method and system for reducing the effects of clutters to provide increased performance for radars.